In mobile radio systems, the signals are propagated via a number of propagation paths between the transmitter and the receiver. The influence of this multipath propagation on the signal may be described in the form of a linear, time-variant transformation. The signal distortion which is caused by the multipath propagation makes correct detection of the transmitted data impossible without a correction mechanism. This correction mechanism, which is referred to as adaptive equalization, is based on continuously repeated measurement of the channel characteristics of the transmission channel (channel estimation). The information which is determined about the transmission channel during the channel estimation process is used for equalization of the received signal.
In order to allow channel estimation in the receiver, the transmitter transmits symbols which are known in the receiver. These symbols which are known in the receiver are referred to as pilot symbols. The receiver receives the distorted pilot symbols which are transmitted via the channel and compares them with the transmitted pilot symbols. The quotient of the pilot symbols as received via a specific propagation path and the known pilot symbols then results in the channel coefficient which is applicable to the relevant propagation path at that time. With optimum channel knowledge, the rotation and magnitude change which occur in the received complex-value symbol in the transmission path can be compensated for. This allows the data to be detected with a lower bit error rate.
Various known algorithms are available for channel estimation. The best known algorithm for channel estimation is signal-matched filtering (MF: Matched Filter). Signal-matched filtering does not require any knowledge about the statistical characteristics of the channel, and has a maximum signal-to-interference and noise ratio as the optimality criterion. Wiener filtering is an example of a channel estimation algorithm which takes account of statistical characteristics of the channel, such as those which are described by an appropriate stochastic channel model, in the estimation process. The optimality criterion for Wiener filtering is to minimize the mean square estimation error MMSE (Minimum Mean Square Error).
In practice, the channel estimation process is carried out as follows. In the following text, the sequence of transmitted complex pilot symbols is identified by p1, p2, for a single transmission path. The transmission channel multiplies the pilot symbol pk by the complex channel coefficient ck. Noise nk is added to this, so that the symbol which is received via the propagation path under consideration is in the form yk=pk*ck+nk, k=1, 2, . . . , where k is the index for the discrete time at the symbol clock rate. The channel estimation process is normally carried out in two steps. The first step comprises correlation of the received pilot symbol with the transmitted pilot symbol, that is to say calculation of the quotient xk=yk/pk. If there is no noise (nk=0), then xk=ck. The quotient xk may be referred to as the unfiltered estimation value. In a second step, the sequence of unfiltered channel estimation values xk is now filtered in order to reduce the noise component.
Two approaches are used, in particular, for the filtering of the sequence xk:                filtering by means of an FIR (Finite Impulse Response) filter with a specific filter length. As is known, FIR filters are non-recursive filters. The filter is constructed on the basis of an optimality criterion, which is known from statistical signal theory. In particular, an FIR filter may be used as an LMMSE (Linear Minimum Mean Square Error) estimator. The filter coefficients for the FIR filter are in this case calculated and are appropriately defined on the basis of the optimality criterion MMSE. One example of an FIR filter such as this which minimizes the mean square error is a Wiener filter.        The filtering process is carried out by means of an IIR (Infinite Impulse Response) filter. As is known, an IIR filter is a recursive filter. IIR filters are frequently used which carry out only a single recursion, that is to say they have a single delay element.        
In both cases, sub-optimum variants are frequently chosen in the implementation. In particular, the optimum filter on the basis of the above criteria is dependent on the signal-to-interference and noise ratio SINR of the respective propagation path, and on the relative speed between the transmitter and the receiver. IIR filters are used in particular for low relative speeds. However, IIR filters are not very suitable for also being used at the same time as channel estimators for high speeds with sufficient accuracy. FIR filters must therefore be used between the transmitter and the receiver for high relative speeds. In order to avoid the complexity of the filter unit becoming excessive, predefined coefficient sets may be used for the FIR filter and for the IIR filter, which cover a sufficiently wide range of SINR and speeds.
The publication by J. Baltersee et al., “Performance Analysis of Phasor Estimation Algorithms for a FDD-UMTS RAKE Receiver”, IEEE 6th Int. Symp. on Spread-Spectrum Tech. & Appli., NJIT, New Jersey, USA, Sep. 6-8, 2000 describes a channel estimator for a Rake receiver whose filter unit has an FIR filter with 15 filter coefficients for high speeds (120 km/h) and a recursive LMS Kalman filter of the IIR type for lower speeds.